scholarly journals The brain as a complex network: assessment of EEG-based functional connectivity patterns in patients with childhood absence epilepsy

2020 ◽  
Vol 22 (5) ◽  
pp. 519-530
Author(s):  
Eva Paradiž Leitgeb ◽  
Marko Šterk ◽  
Timotej Petrijan ◽  
Peter Gradišnik ◽  
Marko Gosak
Keyword(s):  
Functional Connectivity ◽  
Complex Network ◽  
Absence Epilepsy ◽  
Childhood Absence Epilepsy ◽  
Connectivity Patterns ◽  
The Brain

scholarly journals Source Space Analysis of Event-Related Dynamic Reorganization of Brain Networks

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Andreas A. Ioannides ◽  
Stavros I. Dimitriadis ◽  
George A. Saridis ◽  
Marotesa Voultsidou ◽  
Vahe Poghosyan ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Brain Activity ◽  
Clinical Applications ◽  
Brain Areas ◽  
Connectivity Patterns ◽  
The Brain ◽  
Typical Subject ◽  
Different Parts ◽  
Neuroimaging Techniques

How the brain works is nowadays synonymous with how different parts of the brain work together and the derivation of mathematical descriptions for the functional connectivity patterns that can be objectively derived from data of different neuroimaging techniques. In most cases static networks are studied, often relying on resting state recordings. Here, we present a quantitative study of dynamic reconfiguration of connectivity for event-related experiments. Our motivation is the development of a methodology that can be used for personalized monitoring of brain activity. In line with this motivation, we use data with visual stimuli from a typical subject that participated in different experiments that were previously analyzed with traditional methods. The earlier studies identified well-defined changes in specific brain areas at specific latencies related to attention, properties of stimuli, and tasks demands. Using a recently introduced methodology, we track the event-related changes in network organization, at source space level, thus providing a more global and complete view of the stages of processing associated with the regional changes in activity. The results suggest the time evolving modularity as an additional brain code that is accessible with noninvasive means and hence available for personalized monitoring and clinical applications.

scholarly journals Tools of the trade: Estimating time-varying connectivity patterns from fMRI data

2020 ◽  
Author(s):  
Armin Iraji ◽  
Ashkan Faghiri ◽  
Noah Lewis ◽  
Zening Fu ◽  
Srinivas Rachakonda ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Network Connectivity ◽  
Extended Period ◽  
Brain Disorders ◽  
Dynamic Nature ◽  
Domain Experts ◽  
Connectivity Patterns ◽  
Analytical Approaches ◽  
Dynamic Metrics ◽  
The Brain

Given the dynamic nature of the brain, there has always been a motivation to move beyond “static” functional connectivity, which characterizes functional interactions over an extended period of time. Progress in data acquisition and advances in analytical neuroimaging methods now allow us to assess the whole brain’s dynamic functional connectivity (dFC) and its network-based analog, dynamic functional network connectivity (dFNC) at the macroscale (mm) using fMRI. This has resulted in the rapid growth of analytical approaches, some of which are very complex, requiring technical expertise that could daunt researchers and neuroscientists. Meanwhile, making real progress toward understanding the association between brain dynamism and brain disorders can only be achieved through research conducted by domain experts, such as neuroscientists and psychiatrists. This article aims to provide a gentle introduction to the application of dFC. We first explain what dFC is and the circumstances under which it can be used. Next, we review two major categories of analytical approaches to capture dFC. We discuss caveats and considerations in dFC analysis. Finally, we walk readers through an openly accessible toolbox to capture dFC properties and briefly review some of the dynamic metrics calculated using this toolbox.

scholarly journals Large-scale intrinsic connectivity is consistent across varying task demands

2018 ◽  
Author(s):  
Paulina Kieliba ◽  
Sasidhar Madugula ◽  
Nicola Filippini ◽  
Eugene P. Duff ◽  
Tamar R. Makin
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Large Scale ◽  
Brain Activation ◽  
Whole Brain ◽  
Activation Patterns ◽  
Intrinsic Connectivity ◽  
Connectivity Patterns ◽  
Brain Functional Connectivity ◽  
The Brain

AbstractMeasuring whole-brain functional connectivity patterns based on task-free (‘restingstate’) spontaneous fluctuations in the functional MRI (fMRI) signal is a standard approach to probing habitual brain states, independent of task-specific context. This view is supported by spatial correspondence between task- and rest-derived connectivity networks. Yet, it remains unclear whether intrinsic connectivity observed in a resting-state acquisitions is persistent during task. Here, we sought to determine how changes in ongoing brain activation, elicited by task performance, impact the integrity of whole-brain functional connectivity patterns. We employed a ‘steadystates’ paradigm, in which participants continuously executed a specific task (without baseline periods). Participants underwent separate task-based (visual, motor and visuomotor) or task-free (resting) steady-state scans, each performed over a 5-minute period. This unique design allowed us to apply a set of traditional resting-state analyses to various task-states. In addition, a classical fMRI block-design was employed to identify individualized brain activation patterns for each task, allowing to characterize how differing activation patterns across the steady-states impact whole-brain intrinsic connectivity patterns. By examining correlations across segregated brain regions (nodes) and the whole brain (using independent component analysis), we show that the whole-brain network architecture characteristic of the resting-state is robustly preserved across different steady-task states, despite striking inter-task changes in brain activation (signal amplitude). Subtler changes in functional connectivity were detected locally, within the active networks. Together, we show that intrinsic connectivity underlying the canonical resting-state networks is relatively stable even when participants are engaged in different tasks and is not limited to the resting-state.New and NoteworthyDoes intrinsic functional connectivity (FC) reflect the canonical or transient state of the brain? We tested the consistency of the intrinsic connectivity networks across different task-conditions. We show that despite local changes in connectivity, at the whole-brain level there is little modulation in FC patterns, despite profound and large-scale activation changes. We therefore conclude that intrinsic FC largely reflects the a priori habitual state of the brain, independent of the specific cognitive context.

Cognitive impairment in children with childhood absence epilepsy (review)

2020 ◽  
Vol 18 (5) ◽  
pp. 25-30
Author(s):  
F. I. GUSYAKOV ◽  
◽  
D. V.MOROZOV MOROZOV ◽  
E. A. MOROZOVA ◽  
V. F. PRUSAKOV ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Antiepileptic Drugs ◽  
Clinical Features ◽  
Epileptiform Activity ◽  
Absence Epilepsy ◽  
School Age ◽  
Childhood Absence Epilepsy ◽  
Epileptic Discharges ◽  
Neuropsychological Impairments ◽  
The Brain

The review is devoted to common epilepsy of childhood — childhood absence epilepsy. This epileptic syndrome debuts at pre-school age and is characterized by frequent seizures which may be underestimated by doctors due to the clinical features of a seizure. The seizures can debilitate a child and restrict socialization, as specific comorbidities in cognitive field are very common for child absence epilepsy. The etiology of neropsychiatric comorbidities is different. Seizures, epileptic discharges, as well as interictal epileptiform activity in the brain may cause neuropsychological impairments in children. Antiepileptic drugs are important in the development of neuropsychological impairments. The aim of this review was to actualize information on childhood absence epilepsy and stress the necessity to pay practitioner’s attention to common comorbidities in cognitive field.

Caudal inferior parietal cortex supports the hypothesis about a modulating cortical area

2022 ◽  
Author(s):  
Fatemeh Tabassi Mofrad ◽  
Niels O. Schiller
Keyword(s):  
Functional Connectivity ◽  
Cognitive Control ◽  
Parietal Cortex ◽  
Resting State ◽  
Cortical Area ◽  
Control Area ◽  
Brain Areas ◽  
Inferior Parietal Cortex ◽  
Connectivity Patterns ◽  
The Brain

The cytoarchitectonically tripartite organization of the inferior parietal cortex (IPC) into the rostral, the middle and the caudal clusters has been generally ignored when associating different functions to this part of the cortex, resulting in inconsistencies about how IPC is understood. In this study, we investigated the patterns of functional connectivity of the caudal IPC in a task requiring cognitive control of language, using multiband EPI. This part of the cortex demonstrated functional connectivity patterns dissimilar to a cognitive control area and at the same time the caudal IPC showed negative functional associations with both task-related brain areas and the precuneus cortex, which is active during resting state. We found evidence suggesting that the traditional categorization of different brain areas into either task-related or resting state-related networks cannot accommodate the functions of the caudal IPC. This underlies the hypothesis about a modulating cortical area proposing that its involvement in task performance, in a modulating manner, is marked by deactivation in the patterns of functional associations with parts of the brain that are recognized to be involved in doing a task, proportionate to task difficulty; however, their patterns of functional connectivity in some other respects do not correspond to the resting state-related parts of the cortex.

Altered intrinsic functional connectivity of the salience network in childhood absence epilepsy

2014 ◽  
Vol 339 (1-2) ◽  
pp. 189-195 ◽  
Author(s):  
Cheng Luo ◽  
Tianhua Yang ◽  
Shipeng Tu ◽  
Jiayan Deng ◽  
Dongbo Liu ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Absence Epilepsy ◽  
Salience Network ◽  
Childhood Absence Epilepsy ◽  
Intrinsic Functional Connectivity

scholarly journals Tools of the trade: estimating time-varying connectivity patterns from fMRI data

2020 ◽  
Author(s):  
Armin Iraji ◽  
Ashkan Faghiri ◽  
Noah Lewis ◽  
Zening Fu ◽  
Srinivas Rachakonda ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Network Connectivity ◽  
Extended Period ◽  
Brain Disorders ◽  
Dynamic Nature ◽  
Domain Experts ◽  
Connectivity Patterns ◽  
Analytical Approaches ◽  
Dynamic Metrics ◽  
The Brain

Abstract Given the dynamic nature of the brain, there has always been a motivation to move beyond ‘static’ functional connectivity, which characterizes functional interactions over an extended period of time. Progress in data acquisition and advances in analytical neuroimaging methods now allow us to assess the whole brain’s dynamic functional connectivity (dFC) and its network-based analog, dynamic functional network connectivity at the macroscale (mm) using fMRI. This has resulted in the rapid growth of analytical approaches, some of which are very complex, requiring technical expertise that could daunt researchers and neuroscientists. Meanwhile, making real progress toward understanding the association between brain dynamism and brain disorders can only be achieved through research conducted by domain experts, such as neuroscientists and psychiatrists. This article aims to provide a gentle introduction to the application of dFC. We first explain what dFC is and the circumstances under which it can be used. Next, we review two major categories of analytical approaches to capture dFC. We discuss caveats and considerations in dFC analysis. Finally, we walk readers through an openly accessible toolbox to capture dFC properties and briefly review some of the dynamic metrics calculated using this toolbox.

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